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Richard Myers, Richard C. Wilson, Edwin R. Hancock, "Bayesian Graph Edit Distance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 628635, June, 2000.  
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@article{ 10.1109/34.862201, author = {Richard Myers and Richard C. Wilson and Edwin R. Hancock}, title = {Bayesian Graph Edit Distance}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {22}, number = {6}, issn = {01628828}, year = {2000}, pages = {628635}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.862201}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Bayesian Graph Edit Distance IS  6 SN  01628828 SP628 EP635 EPD  628635 A1  Richard Myers, A1  Richard C. Wilson, A1  Edwin R. Hancock, PY  2000 KW  Graph matching KW  editdistance KW  Bayesian KW  MAP estimation KW  stereo images. VL  22 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—This paper describes a novel framework for comparing and matching corrupted relational graphs. The paper develops the idea of editdistance originally introduced for graphmatching by Sanfeliu and Fu [1]. We show how the Levenshtein distance can be used to model the probability distribution for structural errors in the graphmatching problem. This probability distribution is used to locate matches using MAP label updates. We compare the resulting graphmatching algorithm with that recently reported by Wilson and Hancock. The use of editdistance offers an elegant alternative to the exhaustive compilation of label dictionaries. Moreover, the method is polynomial rather than exponential in its worstcase complexity. We support our approach with an experimental study on synthetic data and illustrate its effectiveness on an uncalibrated stereo correspondence problem. This demonstrates experimentally that the gain in efficiency is not at the expense of quality of match.
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